基于扰乱欧勒-拉格朗奇的神经动力学方法,用于非平滑的非合作性游戏,并有通信延迟
Jianing Chen1, Shuangyu Liu1, Yuhan Xue2
1Department of Mathematics, Harbin Institute of Technology (Weihai), Weihai, 264209, PR China.
概括
这项研究提出了一种分布式神经动力学方法,用于在非平滑的游戏中寻找变化的通用纳什平衡. 该方法增强了对干扰和通信延迟的稳定性,确保趋同到平衡.
科学领域:
- 控制理论 控制理论
- 游戏理论 游戏理论
- 分布式系统 分布式系统
背景情况:
- 不平滑的非合作游戏提出了复杂的寻求平衡的挑战.
- 现有的方法在分布式环境中难以应对干扰和通信延迟.
研究的目的:
- 开发一种分布式神经动力学方法,用于在非平滑游戏中寻求变量通用纳什平衡 (vGNE).
- 解决分布式游戏系统中干扰和通信延迟的挑战.
主要方法:
- 一种分布式vGNE寻找的神经动力学方法 (vGSNA) 基于一个被扰乱的欧勒-拉格朗日 (EL) 系统.
- 引入了一种新的干扰观察器,用于增强干扰排斥.
- 利亚普诺夫-克拉索夫斯基函数用于在时间变化的延迟下进行稳定性分析.
主要成果:
- 在有限干扰下,vGSNA证明了对vGNE的可靠计算.
- 参与者的行动与vGNE的趋同已被证明,即使有一般限定的变化时间的通信延迟.
- 该方法的效率通过电力市场游戏模拟来验证.
结论:
- 拟议的vGSNA有效地解决了分布式vGNE对非平滑游戏的搜索问题.
- 干扰观察器和稳定性分析确保在动态的,延迟的环境中可靠的性能.
- 该方法适用于现实世界的场景,如电力市场建模.
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